Geography and Sustainability最新文献

Spatial heterogeneity in machine learning-based poverty mapping: Where do models underperform? 基于机器学习的贫困映射中的空间异质性：模型在哪里表现不佳？

IF 8 1区环境科学与生态学 Q1 GEOGRAPHY, PHYSICAL

Geography and Sustainability

Pub Date : 2026-04-01 Epub Date: 2026-01-15 DOI: 10.1016/j.geosus.2026.100413

Yating Ru , Elizabeth Tennant , David S. Matteson , Christopher B. Barrett

Accurately locating poor populations is increasingly urgent as global poverty reduction has stalled under the combined pressures of conflicts, climate shocks, rising food prices, pandemics, and growing inequality. Recent studies harnessing geospatial big data and machine learning (ML) have significantly advanced poverty mapping, enabling granular and timely welfare estimates in traditionally data-scarce regions. While much of the existing research has focused on overall out-of-sample predictive performance, there is a lack of understanding regarding where such models underperform and whether key spatial relationships might vary across places. This study investigates spatial heterogeneity in ML-based poverty mapping in East Africa, testing whether spatial regression and ML techniques produce more unbiased predictions. We find that extrapolation into unsurveyed areas suffers from biases that spatial methods do not resolve; welfare is overestimated in impoverished regions, rural areas, and single sector-focused economies, whereas it tends to be underestimated in wealthier, urbanized, and diversified economies. Even as spatial models improve overall predictive accuracy, enhancements in traditionally underperforming areas remain marginal. This underscores the need for more representative training datasets and better remotely sensed proxies, especially for poor and rural regions, in future research related to ML-based poverty mapping. For development agencies, the findings caution against treating ML-based outputs as neutral or universally reliable, highlighting instead the need to pair technical advances with investments in inclusive data collection, integration of spatial theory, and institutional strategies that address structural data inequalities.

在冲突、气候冲击、粮食价格上涨、流行病和不平等加剧的综合压力下，全球减贫工作停滞不前，准确定位贫困人口日益紧迫。最近利用地理空间大数据和机器学习（ML）的研究显著推进了贫困制图，使传统数据稀缺地区能够进行精细和及时的福利估算。虽然现有的大部分研究都集中在整体样本外预测性能上，但对于这些模型在哪些方面表现不佳以及关键的空间关系是否会因地而异，人们缺乏理解。本研究调查了东非基于机器学习的贫困地图的空间异质性，测试了空间回归和机器学习技术是否能产生更公正的预测。我们发现，对未调查地区的外推存在空间方法无法解决的偏差；在贫困地区、农村地区和以单一部门为重点的经济体，福利往往被高估，而在富裕、城市化和多元化的经济体，福利往往被低估。即使空间模型提高了整体预测的准确性，但在传统上表现不佳的领域的增强仍然微乎其微。这强调了在未来与基于机器学习的贫困制图相关的研究中，需要更有代表性的训练数据集和更好的遥感代理，特别是针对贫困和农村地区。对于发展机构而言，研究结果告诫不要将基于机器学习的产出视为中立或普遍可靠，而是强调需要将技术进步与包容性数据收集、空间理论整合和解决结构性数据不平等的制度战略方面的投资结合起来。

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引用次数: 0

Hidden costs of a thriving Yellow River: Severe groundwater depletion 黄河繁荣的隐性代价：严重的地下水枯竭

IF 8 1区环境科学与生态学 Q1 GEOGRAPHY, PHYSICAL

Geography and Sustainability

Pub Date : 2026-04-01 Epub Date: 2026-01-06 DOI: 10.1016/j.geosus.2026.100407

Mengzhu Liu , Yilin Shen , Ying Guo , Lili Yu , Yongqing Qi , Bojie Fu , Yanjun Shen

Groundwater storage (GWS) is essential for supporting agricultural irrigation and revegetation in the water-scarce Yellow River Basin (YRB). Early studies have mainly focused on the impacts of revegetation on GWS, and rarely consider the influences of agricultural irrigation and other human activities, rendering the driving mechanisms of GWS unclear. Here we used NASA’s Gravity Recovery and Climate Experiment (GRACE) satellite data, the PCR-GLOBWB2 hydrologic model, and an Long Short-Term Memory (LSTM) machine learning approach to reveal changes, driving mechanisms, and future trends in GWS in the YRB. Results show that GWS in the YRB decreased by ∼101 Gt in 2003−2020, roughly 24 times the Yellow River’s flow into the sea in 2000. Notably, GWS depletion (−7.7 mm/yr) dominates the observed terrestrial water storage (TWS) losses (−6.0 mm/yr) and accounts for >100% of the net TWS decline. Storage losses are largely explained by increases in evapotranspiration (+6.0 mm/yr) driven by revegetation and agricultural irrigation. This is evident in higher evapotranspiration rates (+3 mm/yr) observed in heavily revegetated areas, with irrigation showing an estimated contribution of −6.6 mm/yr on GWS by the PCR-GLOBWB2 model. GWS losses are projected to persist until 2060 by the LSTM model, with a total storage loss of ∼237 Gt. With GWS declining and natural recharge growth lagging behind the rise in groundwater demand, the YRB confronts a future of groundwater deficits. The study suggests that although groundwater extraction for agricultural and ecological benefits might appear helpful to the region in the short term, this trajectory is physically unsustainable and detrimental to the water-scarce Yellow River.

在水资源匮乏的黄河流域，地下水蓄能对农业灌溉和植被恢复具有重要的支持作用。早期的研究主要集中在植被恢复对GWS的影响上，很少考虑农业灌溉等人类活动的影响，导致GWS的驱动机制不明确。本文利用NASA重力恢复与气候实验（GRACE）卫星数据、PCR-GLOBWB2水文模型和长短期记忆（LSTM）机器学习方法揭示了YRB GWS的变化、驱动机制和未来趋势。结果表明，2003 ~ 2020年长江流域的GWS减少了~ 101 Gt，约为2000年黄河入海流量的24倍。值得注意的是，GWS损耗（- 7.7 mm/yr）主导了观测到的陆地水储存（TWS）损失（- 6.0 mm/yr），占TWS净下降的100%。储存损失在很大程度上可以解释为由植被恢复和农业灌溉驱动的蒸散量增加（+6.0 mm/年）。这一点在重植被地区观察到的更高的蒸散速率（+3毫米/年）中得到了证明，PCR-GLOBWB2模型估计灌溉对GWS的贡献为- 6.6毫米/年。根据LSTM模型，预计GWS损失将持续到2060年，总储水量损失约为237gt。随着GWS的下降和自然补给的增长滞后于地下水需求的增长，长江三角洲将面临地下水短缺的未来。该研究表明，尽管从农业和生态效益的角度来看，地下水开采在短期内可能对该地区有所帮助，但这种轨迹在物理上是不可持续的，并且对缺水的黄河有害。

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引用次数: 0

Spatio-temporal responses of ecological resilience to urbanization in five Great Lakes Regions (GLRs) in China and implications for building resilient GLRs 中国五大湖生态弹性对城市化的时空响应及其对建设弹性大湖区的启示

IF 8 1区环境科学与生态学 Q1 GEOGRAPHY, PHYSICAL

Geography and Sustainability

Pub Date : 2026-02-01 Epub Date: 2025-11-28 DOI: 10.1016/j.geosus.2025.100395

Tongning Li , Guoen Wei , Minghui Xu , Daozheng Li , Weifeng Deng , Yaobin Liu , Bao-Jie He

Great Lakes Regions (GLRs) in China often confront landscape fragmentation, wetland degradation, and ecological resilience (ER) losses owing to extensive and intensive urbanization. In GLRs, however, the ER responses to urbanization remain unclear. This study explored the spatiotemporal evolution of ER and urbanization in five GLRs in China to analyze the ER dynamic patterns along center−lakeside−periphery gradient. The Spatial Durbin Model (SDM) and Panel Threshold Model (PTM) were combined to reveal the spillover and threshold effects of urbanization in five GLRs. The results indicate that the ER in five GLRs declined with a rate of 21 % from 2000 to 2020. There was a clear “center-periphery” contraction trend with low ER areas primarily spreading to human activity-concentrated regions such as lakesides, riversides, and road networks. Driven by economic and land urbanization, the average urbanization level increased from 0.06 to 0.13, where lakesides, riversides, and road networks were key areas undergoing expansion. The urbanization showed a noticeable negative spatial spillover effect on ER. Away from central lakes, the negative impacts on ER exhibited a two-phase decrease with the threshold of 81 km. This study contributes to the understanding of human-environment interactions by examining the ecological resilience response process of GLRs under the impact of urbanization. Based on a multidimensional “center−lakeside−periphery” analytical model, this study provides a strategic framework for ecological construction in GLRs in China, promoting sustainable development and adaptive capacity in vulnerable areas.

中国大湖区由于广泛和集约化的城市化，经常面临景观破碎化、湿地退化和生态恢复力丧失的问题。然而，在glr中，ER对城市化的响应仍不清楚。本研究以中国5个大湖区为研究对象，探讨了城市内能和城市化的时空演变特征，并分析了城市内能在中心-湖滨-外围梯度上的动态格局。结合空间Durbin模型（SDM）和面板阈值模型（PTM），分析了5个大城市群城市化的溢出效应和阈值效应。结果表明，从2000年到2020年，5个glr的ER以21%的速度下降。低ER区主要向湖滨、河滨、道路等人类活动集中的区域扩散，呈现明显的“中心-边缘”收缩趋势。在经济城市化和土地城市化的推动下，平均城市化水平从0.06提高到0.13，其中湖滨、滨江和道路网络是重点扩展区域。城市化对生态环境具有显著的负空间溢出效应。在远离中心湖泊的地方，对生态承载力的负面影响呈两期递减，阈值为81 km。本研究通过考察城市化影响下glr的生态弹性响应过程，有助于理解人与环境的相互作用。基于“中心—湖滨—边缘”的多维分析模型，为中国湿地生态建设提供了战略框架，以促进脆弱地区的可持续发展和适应能力。

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引用次数: 0

Recent widespread forest expansion and densification in Southwest China 中国西南地区近期广泛的森林扩张和密实化

IF 8 1区环境科学与生态学 Q1 GEOGRAPHY, PHYSICAL

Geography and Sustainability

Pub Date : 2026-02-01 Epub Date: 2025-12-29 DOI: 10.1016/j.geosus.2025.100404

Daoming Ma , Yang Yu , Ming Gong , Zhiqiang Zhang , Steven A Kannenberg

Large-scale afforestation and forest conservation policies have been widely implemented in Southwest China over past decades. These efforts have significantly protected the remaining long-established forests in the region and greatly expanded forested areas. Utilizing nearly 30 years of satellite time-series data, we reveal that the region’s enhanced carbon sequestration (3 × 10¹² g·C annually) is primarily driven by crucial changes in forest structure and age, occurring alongside a nearly 120 % increase in forested land area. We observe that dense forests maintain a rapid growth rate of approximately 2.5 % annually for carbon sequestration in the initial years after establishment. However, this growth rate decelerates with increasing apparent forest age. Meanwhile, the densification (modeled as an increasing forest probability) rate of forests reaches its peak growth during the 10–20 year period, sustaining a high annual growth rate of about 1.8 %. We also find that improvements in forest structure, particularly the increasing of forest canopy density and apparent forest age coupled with a notable reduction in forest fragmentation, are also the main driving factors for the enhanced carbon sequestration capacity. Based on these findings, we conclude that forest restoration policies in Southwest China have been successful not only in facilitating large-scale forest growth in Southwest China but, more critically, in promoting the structural maturation (e.g., densification and reduced fragmentation) that is essential for enhancing the region’s carbon sink capacity and its resilience.

在过去的几十年里，中国西南地区广泛实施了大规模的造林和森林保护政策。这些努力极大地保护了该地区现存的历史悠久的森林，并大大扩大了森林面积。利用近30年的卫星时间序列数据，我们发现该地区碳固存的增强（每年3 × 1012 g·C）主要是由森林结构和年龄的关键变化驱动的，与此同时森林面积增加了近120%。我们观察到，茂密的森林在建立后的最初几年里，其固碳量保持每年约2.5%的快速增长率。然而，这一增长率随着森林表观年龄的增加而减慢。与此同时，森林的密实化（模拟为森林概率的增加）率在10-20年期间达到高峰，保持1.8%左右的高年增长率。森林结构的改善，特别是林冠密度和林龄的增加，以及森林破碎化程度的显著降低，也是森林固碳能力增强的主要驱动因素。基于这些发现，我们得出结论，中国西南地区的森林恢复政策不仅成功地促进了西南地区森林的大规模生长，而且更重要的是，促进了结构成熟（如密实化和减少破碎化），这对增强该地区的碳汇能力和恢复力至关重要。

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引用次数: 0

A data- and expert-driven framework for establishing land cover–related essential variables for SDG monitoring and assessment 一个数据和专家驱动的框架，用于为可持续发展目标监测和评估建立与土地覆盖相关的基本变量

IF 8 1区环境科学与生态学 Q1 GEOGRAPHY, PHYSICAL

Geography and Sustainability

Pub Date : 2026-02-01 Epub Date: 2025-11-28 DOI: 10.1016/j.geosus.2025.100397

Hao Wu , Ping Zhang , Jun Chen , Songnian Li , Jing Li , Shu Peng , Dongyang Hou , Jun Zhang , Hao Chen

Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals (SDGs). Although land cover information has long been recognized as an essential component for monitoring SDGs, a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist. Therefore, we propose a novel expert- and data-driven framework for identifying, refining, and selecting a priority list of Essential Land cover-related Variables for SDGs (ELcV4SDGs). This framework integrates methods including expert knowledge-based analysis, clustering of variables with similar attributes, and quantified index calculation to establish the priority list. Applying the framework to 15 specific SDG indicators, we found that the ELcV4SDGs priority list comprises three main categories, type and structure, pattern and intensity, and process and evolution of land cover, which are further divided into 19 subcategories and ultimately encompass 50 general variables. The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment, thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local, national, and global levels.

对联合国2030年可持续发展议程进行持续和明确的空间监测，对于有效跟踪实现全球可持续发展目标的进展至关重要。虽然土地覆盖信息长期以来一直被认为是监测可持续发展目标的重要组成部分，但目前还没有一个标准化的科学框架来确定与土地覆盖相关的基本变量并对其进行优先排序。因此，我们提出了一个新的专家和数据驱动的框架，用于识别、提炼和选择可持续发展目标的基本土地覆盖相关变量优先列表（ELcV4SDGs）。该框架集成了基于专家知识的分析、相似属性变量聚类和量化指标计算等方法来建立优先级列表。将该框架应用于15个具体的可持续发展目标指标，我们发现elcv4sdg优先级列表包括土地覆盖的类型和结构、格局和强度、过程和演变三个主要类别，并进一步划分为19个子类别，最终包含50个一般变量。ELcV4SDGs将支持详细的空间监测，并加强其在可持续发展目标监测和评估中的科学应用，从而指导未来的可持续发展目标优先行动，并为决策提供信息，从而在地方、国家和全球层面推进2030年可持续发展目标议程。

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引用次数: 0

AI ethics in geoscience: Toward trustworthy and responsible innovation 地球科学中的人工智能伦理：走向可信和负责任的创新

IF 8 1区环境科学与生态学 Q1 GEOGRAPHY, PHYSICAL

Geography and Sustainability

Pub Date : 2026-02-01 Epub Date: 2026-01-19 DOI: 10.1016/j.geosus.2026.100414

Jinran Wu , Xin Tian , You-Gan Wang , Tong Li , Qingyang Liu , Yayong Li , Lizhen Cui , Zhuangcai Tian , Jing Xu , Xianzhou Lyu , Yuming Mo

引用次数: 0

International food trade increased the food security gap between high and low economic development groups 国际粮食贸易扩大了高、低经济发展群体之间的粮食安全差距

IF 8 1区环境科学与生态学 Q1 GEOGRAPHY, PHYSICAL

Geography and Sustainability

Pub Date : 2026-02-01 Epub Date: 2025-12-16 DOI: 10.1016/j.geosus.2025.100402

Zihan Xu , Tianyi Wu , Tao Hu , Yanxu Liu , Jian Peng

International trade serves as a crucial pathway for enhancing global food security and equality amid severe food crises worldwide. Under globalization, economic development has profoundly influenced food trade, while disparities in food purchasing power among different economic development groups have led to uneven food security outcomes. However, the varying contributions of international trade to food security across these groups remain to be quantitatively elucidated. This study categorized countries into four economic development groups—high, high-medium, medium-low, and low—and examined changes in their food security scores from 2010 to 2019. The cross-group contributions of international trade to food security across these groups were compared. The results revealed that the food security score of the high economic development group was 9.22 times higher than that of the low economic development group. From 2010 to 2019, the high economic development group exhibited a significant upward trend in food security scores, whereas the low economic development group showed a significant decline. Moreover, international trade contributed significantly to both cross-group and within-group food security in the high economic development group, while its contribution to the low economic development group remained negligible. These findings demonstrated that international trade has further widened the food security gap between the high and low economic development groups, and its limited contribution to the low economic development group has failed to reverse the declining trend in their food security scores. This study quantified the divergent impacts of international trade on food security across economic development groups, providing valuable insights for optimizing global food trade policies—particularly in addressing the food security challenges faced by low econominc development group.

在全球严重的粮食危机中，国际贸易是加强全球粮食安全和平等的重要途径。在全球化背景下，经济发展对粮食贸易产生了深刻影响，而不同经济发展群体之间粮食购买力的差异导致了粮食安全结果的不平衡。然而，国际贸易对这些群体粮食安全的不同贡献仍有待定量阐明。本研究将各国分为高、中高、中低和低四个经济发展组，并研究了2010年至2019年各国粮食安全得分的变化。比较了国际贸易对这些群体粮食安全的跨群体贡献。结果表明：高经济发展群体的粮食安全得分是低经济发展群体的9.22倍；2010 - 2019年，经济发展程度高的国家粮食安全得分呈显著上升趋势，经济发展程度低的国家粮食安全得分呈显著下降趋势。此外，国际贸易对高经济发展群体的跨群体和群体内粮食安全的贡献显著，而对低经济发展群体的贡献几乎可以忽略不计。这些结果表明，国际贸易进一步拉大了高、低经济发展群体之间的粮食安全差距，其对低经济发展群体的有限贡献未能扭转其粮食安全得分下降的趋势。本研究量化了国际贸易对各经济发展群体粮食安全的不同影响，为优化全球粮食贸易政策提供了有价值的见解，特别是在应对低经济发展群体面临的粮食安全挑战方面。

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引用次数: 0

Dynamic patterns and driving factors of productive cropland in Ukraine before and after Russia-Ukraine conflict 俄乌冲突前后乌克兰生产性耕地动态格局及驱动因素

IF 8 1区环境科学与生态学 Q1 GEOGRAPHY, PHYSICAL

Geography and Sustainability

Pub Date : 2026-02-01 Epub Date: 2025-12-04 DOI: 10.1016/j.geosus.2025.100401

Yiliang Li , Kaixuan Yao , Qingxiang Meng , Yujie Wang , Rui Xiao , Yuhang Liu , Sensen Wu , Yansheng Li

Ukraine, as one of the world’s largest agricultural producers and exporters, plays a critical role in global food security. It is essential to understand the spatiotemporal dynamics and drivers of productive cropland in Ukraine, particularly in the context of the 2022 Russia-Ukraine conflict. We provide the first comprehensive assessment of both conflict- and non-conflict-related factors that influenced the distribution and productivity of Ukraine’s cropland from 2013 to 2023. In addition, we propose a novel method using machine learning models to isolate the impact of conflict on cropland. Our findings reveal that, prior to the conflict, the spatial pattern of Ukraine’s mean cultivation rate was primarily shaped by natural factors—such as climate, soil properties, and elevation—whereas socio-economic factors (e.g., GDP and population size) exerted a weaker influence. Interannual dynamics in productive cropland area were largely driven by climate variability. The onset of conflict in 2022 dramatically altered this landscape, with nearly half of the cropland grid cells experiencing a conflict-induced reduction. Notably, almost half of the interannual reduction in productive cropland in 2022 was attributed to climate change. Remarkably, in 2023, the return of displaced populations and favorable climatic conditions in many oblasts contributed to a positive trend in cropland reclamation. Despite this, the total area of productive cropland in 2023 remained below expected levels, due to ongoing conflict and localized droughts. Finally, we highlight the urgent need to adopt a two-pronged approach that addresses both the immediate impacts of conflict and the ongoing threats posed by climate change to ensure the resilience and sustainability of agricultural systems in post-conflict areas.

乌克兰是世界上最大的农业生产国和出口国之一，在全球粮食安全方面发挥着关键作用。了解乌克兰生产性耕地的时空动态和驱动因素至关重要，特别是在2022年俄乌冲突的背景下。我们首次对2013年至2023年影响乌克兰农田分布和生产力的冲突和非冲突相关因素进行了全面评估。此外，我们提出了一种使用机器学习模型的新方法来分离冲突对农田的影响。我们的研究结果表明，在冲突之前，乌克兰平均耕种率的空间格局主要受自然因素（如气候、土壤性质和海拔）的影响，而社会经济因素（如GDP和人口规模）的影响较弱。生产耕地面积的年际动态主要受气候变率驱动。2022年冲突的爆发极大地改变了这一格局，近一半的农田网格单元因冲突而减少。值得注意的是，2022年几乎一半的生产性耕地年际减少归因于气候变化。值得注意的是，在2023年，流离失所人口的返回和许多州有利的气候条件促成了耕地复垦的积极趋势。尽管如此，由于持续的冲突和局部干旱，2023年的耕地总面积仍低于预期水平。最后，我们强调迫切需要采取双管齐下的办法，既应对冲突的直接影响，又应对气候变化带来的持续威胁，以确保冲突后地区农业系统的复原力和可持续性。

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引用次数: 0

A basin-scale water budget calibration method for sustainable water management: A case study in the Loess Plateau, China 可持续水资源管理的流域尺度水收支定标方法——以黄土高原为例

IF 8 1区环境科学与生态学 Q1 GEOGRAPHY, PHYSICAL

Geography and Sustainability

Pub Date : 2026-02-01 Epub Date: 2025-12-03 DOI: 10.1016/j.geosus.2025.100400

Zonghan Ma , Bingfang Wu , Nana Yan , Weiwei Zhu , Mengxiao Li , Hongwei Zeng , Yixuan Wang , Peilin Song , Qiquan Yang , Qingcheng Pan

Accurate water budget closure is critical for sustainable water resource management facing increased pressures from climate change and human activities. Although error reduction methods for individual water balance components have advanced, persistent biases remain due to the independent development of datasets, impacting basin scale water budget balance. In this research, we analyzed the mathematical origin of the bias between water budget components and developed a new basin-scale water balance calibration method that redistributes errors across components while enforcing water balance constraints. Validation confirms systematic improvements, with reduced RMSE (Precipitation: -2.29 mm/month; ET: -1.34 mm/month) and increased R² against in situ observations. Applied to the Jinghe River Basin (2000−2019), the calibrated data reveal declining precipitation (-1.70 mm/year) and evapotranspiration (-1.84 mm/year) alongside slightly increasing runoff (0.20 mm/year in basin depth), signaling a drying trend. Land cover changes—marked by cropland loss (-3,497 km²) and forest (+720 km²) and grassland (+2,776 km²) expansion—reflect improved water consumption requirements by ecosystem, raising concerns for water retention and ecosystem stability. The method is particularly effective for ungauged basins with sparse ground data and underscores the need for integrated land-water management to enhance long-term resilience.

面对气候变化和人类活动日益增加的压力，准确的水预算关闭对于可持续水资源管理至关重要。尽管单个水平衡分量的误差减少方法已经取得了进步，但由于数据集的独立开发，持续存在的偏差仍然存在，影响了流域尺度的水收支平衡。在这项研究中，我们分析了水收支分量之间偏差的数学根源，并开发了一种新的流域尺度水平衡校准方法，该方法在强制水平衡约束的同时，在各分量之间重新分配误差。验证证实了系统的改进，与原位观测相比，RMSE降低（降水：-2.29 mm/月；ET: -1.34 mm/月），R²增加。应用于泾河流域（2000 ~ 2019年），校正后的数据显示，流域降水（-1.70 mm/年）和蒸散量（-1.84 mm/年）减少，径流（流域深度0.20 mm/年）略有增加，呈现干旱趋势。土地覆盖变化——以耕地减少（- 3497平方公里）、森林（+720平方公里）和草地（+ 2776平方公里）扩张为标志——反映了生态系统对水消耗需求的改善，引起了对保水和生态系统稳定性的关注。该方法对地面数据稀少的未测量盆地特别有效，并强调需要进行综合水土管理以增强长期恢复力。

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引用次数: 0

Life cycle environmental impacts and emission reduction pathways of wind power in western China: A scenario-based assessment 中国西部风电全生命周期环境影响与减排路径：基于场景的评估

IF 8 1区环境科学与生态学 Q1 GEOGRAPHY, PHYSICAL

Geography and Sustainability

Pub Date : 2026-02-01 Epub Date: 2025-11-28 DOI: 10.1016/j.geosus.2025.100394

Ning Su , Xiaobing Li , Xin Lyu , Dongliang Dang , Siyu Liu , Chenhao Zhang

Compared with traditional energy sources, wind power has a lower environmental impact. However, emissions are still generated across the life cycle of wind turbines, from production to recycling. As wind power rapidly develops and deployment increases, these impacts are becoming increasingly evident. A comprehensive understanding of these impacts is crucial for sustainable development. Based on the harmonization of previous detailed life cycle assessment (LCA) studies, this study develops a simplified LCA model that estimates the life cycle environmental impacts of wind turbines based on their nominal power. Using this simplified LCA model, we assess the global warming potential (GWP), acidification potential (AP), and cumulative energy demand (CED) of wind power at the regional scale for 2022 and under three future scenarios (high-power wind turbine promotion, reduced wind curtailment, and a comprehensive development scenario). The results indicate that in 2022, the life cycle GWP, AP, and CED of wind power in western China were 10.76 g CO₂ eq/kWh, 0.177 g SO₂ eq/kWh, and 17.6 kJ/kWh, respectively. Scenario simulations suggest that reducing wind curtailment is the most effective approach for reducing emissions in Inner Mongolia, Gansu, Qinghai, Ningxia, and Xinjiang, producing average decreases of 8.64 % in GWP, 8.39 % in AP, and 9.26 % in CED. In contrast, for Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Xizang, and Shaanxi, the promotion of high-power wind turbines provides greater environmental benefits than reducing curtailment, producing average decreases of 3.45 %, 3.09 %, and 4.29 % in GWP, AP, and CED, respectively. These findings help clarify the environmental impact of wind power across its life cycle at the regional scale and provide theoretical references for the direction of future wind power development and the formulation of related policies.

与传统能源相比，风力发电对环境的影响较小。然而，在风力涡轮机的整个生命周期中，从生产到回收，仍然会产生排放。随着风电的快速发展和部署的增加，这些影响变得越来越明显。全面了解这些影响对可持续发展至关重要。基于先前详细的生命周期评估（LCA）研究的统一，本研究开发了一个简化的LCA模型，该模型基于其标称功率估计风力涡轮机的生命周期环境影响。利用该简化的LCA模型，我们评估了2022年区域尺度上风电的全球变暖潜势（GWP）、酸化潜势（AP）和累积能源需求（CED），以及未来三种情景（大功率风力发电推广、减少弃风和综合开发情景）。结果表明，2022年中国西部风电全生命周期GWP、AP和CED分别为10.76 g CO2 eq/kWh、0.177 g SO2 eq/kWh和17.6 kJ/kWh。情景模拟结果表明，减少弃风是内蒙古、甘肃、青海、宁夏和新疆地区最有效的减排方法，平均减少了8.64%的GWP，减少了8.39%的AP和9.26%的CED。相比之下，在广西、重庆、四川、贵州、云南、西藏和陕西，推广大功率风力发电机组的环境效益大于减少弃风，GWP、AP和CED的平均降幅分别为3.45%、3.09%和4.29%。这些发现有助于在区域尺度上理清风电全生命周期的环境影响，为未来风电发展方向和相关政策的制定提供理论参考。

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